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Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury

使用机器学习对急性肾损伤进行早期识别和个性化治疗

基本信息

批准号：
10683199
负责人：
Matthew Michael Churpek
金额：
$ 69.79万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10683199
关键词：
Acute Renal Failure with Renal Papillary Necrosis Admission activity Albuminuria Algorithms Biological Markers Blood Urea Nitrogen Caring Clinical Code Consensus Consult Creatinine Data Detection Development Discrimination Early Diagnosis Early Intervention Electronic Health Record Electronics Future Gold Growth Guidelines Health system Healthcare Systems Hospitalization Hospitals Hour Hypotension Incidence Injury Injury to Kidney Inpatients Intervention Kidney Knowledge LCN2 gene Length of Stay Machine Learning Manuals Measures Methods Modeling Morbidity - disease rate Multi-Institutional Clinical Trial Natural Language Processing Nephrology Neural Network Simulation Outcome Output Pathway interactions Patient Care Patient-Focused Outcomes Patients Pharmaceutical Preparations Phenotype Predictive Value Prevalence Recommendation Research Risk Risk Assessment Risk Factors Running Serum Syndrome TIMP2 gene Text Time Tubular formation United States Urine Work clinical practice cohort comorbidity cost cost estimate deep learning deep learning algorithm deep learning model deep neural network detection method diagnosis standard electronic structure forest high risk hospital readmission improved improved outcome machine learning algorithm machine learning method mortality nephrotoxicity novel novel marker participant enrollment personalized care personalized medicine predictive marker readmission risk risk prediction model risk stratification success tool urinary

项目摘要

PROJECT SUMMARY Acute kidney injury (AKI) occurs in up to 20% of hospitalized patients and is associated with increased risk of readmission, morbidity, and mortality. The estimated annual cost of AKI care in the US is over 10 billion dollars, and, with the incidence rising, these costs will continue to increase. The current gold standards for diagnosing AKI, creatinine and urine output, are often delayed in their recognition of tubular injury. Prior work on AKI has typically focused on patients who have already developed AKI based on these standards, and interventions at this late time point have had mixed success. In contrast, emerging data suggest that intervening earlier can improve outcomes. Therefore, it is critical to optimize the early detection of AKI in hospitalized patients. We have previously developed a machine learning tool to identify patients at high risk of severe (stage 2 or greater) AKI more than a day earlier than clinically apparent using structured electronic health record (EHR) data. Although more accurate than prior methods, it suffers from a high rate of false positives, which limits its value in clinical practice. There is a large amount of valuable information that is stored in unstructured free-text fields (e.g., clinical notes) that could be utilized using natural language processing (NLP) within advanced deep learning neural network models that could significantly improve the detection of early AKI. Furthermore, there are established and emerging kidney injury biomarkers that could be combined with EHR-based models to improve accuracy even further. Finally, it remains unclear what interventions will have the best chance of decreasing the risk for developing severe AKI in high-risk patients. A better understanding of which interventions are of greatest benefit to specific patients is critical for improving the outcomes of patients at risk of AKI. The objective of this project is to develop novel tools to improve the identification and treatment of patients at high risk of AKI using a large, multicenter cohort. In Aim 1, we will use NLP and deep learning algorithms to develop a model to predict severe AKI across four health systems. In Aim 2, we will silently run the best- performing model developed in Aim 1 in real-time to identify high-risk patients. Manual retrospective chart review will be performed on a cohort of the highest risk patients to determine both the proportion of patients who receive guideline-based care as well as the association between receipt of guideline-based care and outcomes. We will also identify novel phenotypes of patients who are particularly helped or harmed by specific guideline-based interventions. Finally, in Aim 3, we will collect kidney injury biomarkers in the highest-risk patients to determine the added value of biomarkers to EHR-based models alone. Our proposal will provide clinicians with new tools to identify patients at risk of AKI earlier and more accurately. It will also provide evidence for which interventions are most likely to improve patient outcomes. This will result in earlier, more personalized care for patients at high risk of AKI, which will lead to decreased costs, morbidity, and mortality.

项目总结急性肾损伤(AKI)发生在高达20%的住院患者中，并与再入院、发病率和死亡率。据估计，美国AKI医疗保健每年的成本超过100亿美元，而且，随着发病率的上升，这些成本将继续增加。当前诊断的黄金标准 AKI，肌酐和尿量，在认识到肾小管损伤时往往会延迟。AKI之前的工作已经完成通常侧重于已经根据这些标准发展为AKI的患者，并在这个最后的时间点取得了好坏参半的结果。相比之下，新出现的数据表明，更早的干预可以改善结果。因此，优化住院患者AKI的早期检测至关重要。我们之前已经开发了一种机器学习工具来识别严重(阶段2)高风险患者或更早)AKI比使用结构化电子健康记录(EHR)的临床表现早一天以上数据。虽然比以前的方法更准确，但它存在着高误报率的问题，这限制了它的在临床实践中的价值。有大量有价值的信息存储在非结构化的自由文本中可在高级深度中使用自然语言处理(NLP)的字段(例如，临床记录) 学习神经网络模型可以显著改善早期AKI的检测。此外，还有是已建立的和新兴的肾脏损伤生物标志物，可以与基于EHR的模型相结合进一步提高精确度。最后，目前还不清楚哪些干预措施最有可能降低高危患者发生严重AKI的风险。更好地了解哪些干预措施对特定患者最大的益处对于改善有AKI风险的患者的预后至关重要。这个项目的目标是开发新的工具来改进患者的识别和治疗。使用大型多中心队列的AKI风险很高。在目标1中，我们将使用NLP和深度学习算法来开发一个模型来预测四个卫生系统的严重AKI。在目标2中，我们将默默地运行最好的- 实时执行AIM 1中开发的模型以识别高危患者。手动回顾图表审查将对高危患者进行队列检查，以确定接受治疗的患者比例以指南为基础的护理以及接受以指南为基础的护理与结果之间的联系。我们会还可以确定特定指南特别帮助或损害的患者的新表型干预措施。最后，在目标3中，我们将收集高危患者的肾损伤生物标志物，以确定仅生物标记物对基于EHR的模型的附加值。我们的建议将为临床医生提供新的工具以便更早、更准确地识别有AKI风险的患者。它还将为哪些干预措施提供证据最有可能改善患者的预后。这将为高危患者带来更早、更个性化的护理 AKI的风险，这将导致降低成本、发病率和死亡率。